Human pose tracking using online latent structured support vector machine

Kai Lung Hua, Irawati Nurmala Sari, Mei Chen Yeh*

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

Tracking human poses in a video is a challenging problem and has numerous applications. The task is particularly difficult in realistic scenes because of several intrinsic and extrinsic factors, including complicated and fast movements, occlusions and lighting changes. We propose an online learning approach for tracking human poses using latent structured Support Vector Machine (SVM). The first frame in a video is used for training, in which body parts are initialized by users and tracking models are learned using latent structured SVM. The models are updated for each subsequent frame in the video sequence. To solve the occlusion problem, we formulate a Prize-Collecting Steiner tree (PCST) problem and use a branch-and-cut algorithm to refine the detection of body parts. Experiments using several challenging videos demonstrate that the proposed method outperforms two state-of-the-art methods.

原文英語
主出版物標題MultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings
編輯Laurent Amsaleg, Gylfi Thór Gudmundsson, Cathal Gurrin, Björn Thór Jónsson, Shin’ichi Satoh
發行者Springer Verlag
頁面626-637
頁數12
ISBN(列印)9783319518107
DOIs
出版狀態已發佈 - 2017
事件23rd International Conference on MultiMedia Modeling, MMM 2017 - Reykjavik, 冰岛
持續時間: 2017 一月 42017 一月 6

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10132 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

其他

其他23rd International Conference on MultiMedia Modeling, MMM 2017
國家/地區冰岛
城市Reykjavik
期間2017/01/042017/01/06

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

指紋

深入研究「Human pose tracking using online latent structured support vector machine」主題。共同形成了獨特的指紋。

引用此